CTmax Data
temp_lat_plot = ctmax_data %>%
ggplot(aes(x = lat, y = collection_temp)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(size = 3) +
labs(x = "Latitude",
y = "Collection Temp. (°C)") +
theme_matt() +
theme(legend.position = "right")
ctmax_temp_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_lat_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = lat, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Latitude",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ctmax_elev_plot = ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = elevation, y = ctmax)) +
geom_smooth(method = "lm", colour = "black") +
geom_point(aes(colour = species),
size = 3) +
labs(x = "Elevation (m)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")
ggpubr::ggarrange(temp_lat_plot, ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 2, ncol = 2, labels = "AUTO")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = ctmax, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
filter(str_detect(species, pattern = "skisto") |
str_detect(species, pattern = "lepto") |
str_detect(species, pattern = "aglao")) %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
group_by(collection_date, species, collection_temp) %>%
summarise(mean_ctmax = mean(ctmax),
ctmax_sd = sd(ctmax),
ctmax_n = n(),
ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>%
ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) +
geom_smooth(method = "lm", se=F, linewidth = 2) +
geom_point(size = 2) +
geom_errorbar(aes(ymin = mean_ctmax - ctmax_se,
ymax = mean_ctmax + ctmax_se),
width = 0.3, linewidth = 1) +
labs(x = "Collection Temp. (°C)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "right")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = size, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Prosome Length (mm)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = collection_temp, y = egg_volume, colour = species)) +
facet_wrap(species~.) +
geom_smooth(method = "lm", colour = "black") +
geom_point() +
labs(x = "Collection Temp. (°C)",
y = "Egg Volume (mm^3)") +
scale_color_manual(values = skisto_cols) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
select(elevation, collection_temp) %>%
distinct() %>%
ggplot(aes(x = elevation, y = collection_temp)) +
geom_point(size = 3) +
labs(x = "Elevation (m)",
y = "Collection Temp. (°C)") +
theme_matt()

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = size, y = ctmax, colour = species)) +
facet_wrap(.~species) +
geom_point(size = 1) +
theme_matt() +
labs(x = "Length (mm)",
y = "CTmax (°C)") +
scale_colour_manual(values = skisto_cols) +
theme(legend.position = "none")

ctmax_data %>%
mutate(species = str_replace(species, "_", " "),
species = str_to_sentence(species)) %>%
ggplot(aes(x = size, y = fecundity, colour = species)) +
facet_wrap(.~species) +
geom_point(size = 1) +
theme_matt() +
labs(x = "Length (mm)",
y = "Fecundity (# eggs)") +
scale_colour_manual(values = skisto_cols) +
theme(legend.position = "none")

ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) +
geom_smooth(method = "lm", formula = y ~ exp(x)) +
geom_point()+
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt()

Data for just Skistodiaptomus pallidus is shown below. Point
color is arranged according to latitude.
ctmax_data %>%
filter(species == "skistodiaptomus_pallidus") %>%
mutate(site = fct_reorder(site, lat, .desc = T)) %>%
# group_by(site) %>%
# summarise(size = mean(size, na.rm = T),
# total_egg_volume = mean(total_egg_volume, na.rm = T)) %>%
ggplot(aes(x = size, y = total_egg_volume)) +
geom_smooth(method = "lm", formula = y ~ exp(x),
colour = "black") +
geom_point(aes(colour = site))+
scale_color_viridis_d(direction = 1,
option = "D") +
labs(x = "Prosome Length (mm)",
y = "Total Egg Volume (mm^3)") +
theme_matt() +
theme(legend.position = "right")

model_data = ctmax_data %>%
mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
genus = tools::toTitleCase(genus),
"doy" = yday(collection_date)) %>%
select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, total_egg_volume, size, ctmax) %>%
filter(genus != "MH") %>%
mutate(total_egg_volume = if_else(is.na(total_egg_volume), 0, total_egg_volume),
collection_temp_sc = scale(collection_temp),
lat_sc = scale(lat),
elevation_sc = scale(elevation),
tev_sc = scale(total_egg_volume))
ctmax_overall.model = lm(data = model_data,
ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume)
drop1(ctmax_overall.model, test = "F")
## Single term deletions
##
## Model:
## ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 479.47 -0.748
## genus 2 142.564 622.03 123.847 72.4015 < 2.2e-16 ***
## collection_temp 1 94.642 574.11 86.243 96.1288 < 2.2e-16 ***
## lat 1 97.843 577.31 88.989 99.3792 < 2.2e-16 ***
## elevation 1 4.769 484.24 2.141 4.8442 0.02821 *
## total_egg_volume 1 20.167 499.64 17.605 20.4842 7.566e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#MuMIn::dredge(ctmax_temp.model)
performance::check_model(ctmax_overall.model)

summary(ctmax_overall.model)
##
## Call:
## lm(formula = ctmax ~ genus + collection_temp + lat + elevation +
## total_egg_volume, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9057 -0.4828 0.1490 0.6352 2.4981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.267e+01 1.102e+00 38.715 < 2e-16 ***
## genusLeptodiaptomus -2.947e+00 2.849e-01 -10.346 < 2e-16 ***
## genusSkistodiaptomus -1.488e+00 2.721e-01 -5.469 7.23e-08 ***
## collection_temp 1.573e-01 1.604e-02 9.805 < 2e-16 ***
## lat -1.987e-01 1.993e-02 -9.969 < 2e-16 ***
## elevation -2.080e-04 9.453e-05 -2.201 0.0282 *
## total_egg_volume 3.923e+01 8.668e+00 4.526 7.57e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9922 on 487 degrees of freedom
## Multiple R-squared: 0.602, Adjusted R-squared: 0.5971
## F-statistic: 122.8 on 6 and 487 DF, p-value: < 2.2e-16
emmeans::emmeans(ctmax_overall.model, specs = "genus") %>%
data.frame() %>%
mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>%
ggplot(aes(genus, y = emmean)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE),
width = 0.2, linewidth = 1) +
labs(x = "") +
theme_matt() +
theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

ctmax_temp.model = lm(data = model_data,
ctmax ~ species * collection_temp)
drop1(ctmax_temp.model,
scope = ~.,
test = "F")
## Single term deletions
##
## Model:
## ctmax ~ species * collection_temp
## Df Sum of Sq RSS AIC F value Pr(>F)
## <none> 334.33 -166.87
## species 4 21.5979 355.92 -143.94 7.7683 4.513e-06 ***
## collection_temp 1 1.2044 335.53 -167.09 1.7328 0.18867
## species:collection_temp 4 8.9849 343.31 -161.76 3.2317 0.01239 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
performance::check_model(ctmax_temp.model)

sp_means = emmeans::emmeans(ctmax_temp.model, "species") %>%
data.frame() %>%
drop_na() %>%
select(species, "ctmax" = emmean)
sp_plast = emmeans::emtrends(ctmax_temp.model, "species", var = "collection_temp") %>%
data.frame() %>%
drop_na() %>%
mutate(species = fct_reorder(species, .x = collection_temp.trend, .desc = T)) %>%
select(species, "plast" = collection_temp.trend)
sp_means %>%
inner_join(sp_plast) %>%
mutate(species = fct_reorder(species, plast, .fun = min)) %>%
ggplot(aes(x = species, y = plast)) +
geom_hline(yintercept = 0) +
geom_bar(stat = "identity") +
theme_matt() +
theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = fecundity, y = site, fill = site)) +
geom_density_ridges(bandwidth = 2,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = size, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.02,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
theme_matt() +
theme(legend.position = "none")

ctmax_data %>%
mutate(group_id = paste(site, species)) %>%
ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) +
geom_density_ridges(bandwidth = 0.3,
jittered_points = TRUE,
point_shape = 21,
point_size = 1,
point_colour = "grey30",
point_alpha = 0.6,
alpha = 0.9,
position = position_points_jitter(
height = 0.1, width = 0)) +
scale_fill_viridis_d(option = "D", direction = -1) +
labs(x = "CTmax (°C)") +
theme_matt() +
theme(legend.position = "none")

---
title: Diaptomid Thermal Limits
date: "`r Sys.Date()`"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          html_preview: false
          toc: true
          toc_depth: 3
---

```{r setup, include=T, message = F, warning = F, echo = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "center",
  fig.path = "../Figures/markdown/",
  dev = c("png", "pdf"),
  message = FALSE,
  warning = FALSE,
  collapse = T
)

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

theme_matt_facets = function(base_size = 18,
                             dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  theme_bw(base_family="sans") %+replace% 
    theme(
      panel.grid = element_blank(),
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      strip.text.x = element_text(size = base_size),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}
```

## Site Map

```{r sampled-sites, fig.width=10, fig.height=6}
coords = ctmax_data %>%
  inner_join(site_data, by = c("site", "lat", "collection_temp")) %>% 
  dplyr::select(site, long, lat, collection_temp, elevation) %>%
  drop_na(collection_temp) %>% 
  distinct()

map_data("world") %>% 
  filter(region %in% c("USA", "Canada")) %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "grey92", colour = "grey40", linewidth = 0.1) + 
  coord_map(xlim = c(-110,-60),
            ylim = c(25, 55)) + 
  geom_point(data = coords,
             mapping = aes(x = long, y = lat, colour = elevation),
             size = 3) +
  scale_colour_viridis_c(option = "G", direction = -1) + 
  labs(x = "Longitude", 
       y = "Latitude",
       colour = "Elev. (m)") + 
  theme_matt() + 
  theme(legend.position = "right")
```

## CTmax Data 

```{r fig.width=14, fig.height=10}
temp_lat_plot = ctmax_data %>% 
  ggplot(aes(x = lat, y = collection_temp)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(size = 3) + 
  labs(x = "Latitude", 
       y = "Collection Temp. (°C)") + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_temp_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_lat_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = lat, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) + 
  labs(x = "Latitude", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ctmax_elev_plot = ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = elevation, y = ctmax)) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point(aes(colour = species), 
             size = 3) +
  labs(x = "Elevation (m)", 
       y = "CTmax (°C)") +
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggpubr::ggarrange(temp_lat_plot, ctmax_temp_plot, ctmax_lat_plot, ctmax_elev_plot, common.legend = T, legend = "right", nrow = 2, ncol = 2, labels = "AUTO")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = ctmax, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=6}
ctmax_data %>% 
  filter(str_detect(species, pattern = "skisto") | 
           str_detect(species, pattern = "lepto") | 
           str_detect(species, pattern = "aglao")) %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  group_by(collection_date, species, collection_temp) %>% 
  summarise(mean_ctmax = mean(ctmax),
            ctmax_sd = sd(ctmax),
            ctmax_n = n(), 
            ctmax_se = ctmax_sd / sqrt(ctmax_n)) %>% 
  ggplot(aes(x = collection_temp, y = mean_ctmax, colour = species)) + 
  geom_smooth(method = "lm", se=F, linewidth = 2) + 
  geom_point(size = 2) + 
  geom_errorbar(aes(ymin = mean_ctmax - ctmax_se, 
                    ymax = mean_ctmax + ctmax_se),
                width = 0.3, linewidth = 1) + 
  labs(x = "Collection Temp. (°C)",
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = size, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Prosome Length (mm)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r fig.width=10, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = collection_temp, y = egg_volume, colour = species)) + 
  facet_wrap(species~.) + 
  geom_smooth(method = "lm", colour = "black") + 
  geom_point() + 
  labs(x = "Collection Temp. (°C)",
       y = "Egg Volume (mm^3)") + 
  scale_color_manual(values = skisto_cols) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r}
ctmax_data %>% 
  select(elevation, collection_temp) %>% 
  distinct() %>% 
  ggplot(aes(x = elevation, y = collection_temp)) + 
  geom_point(size = 3) +
  labs(x = "Elevation (m)", 
       y = "Collection Temp. (°C)") + 
  theme_matt()
```

```{r fig.width=8, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = ctmax, colour = species)) + 
  facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")
```

```{r fig.width=8, fig.height=7}
ctmax_data %>% 
  mutate(species = str_replace(species, "_", " "),
         species = str_to_sentence(species)) %>% 
  ggplot(aes(x = size, y = fecundity, colour = species)) + 
  facet_wrap(.~species) + 
  geom_point(size = 1) + 
  theme_matt() + 
  labs(x = "Length (mm)", 
       y = "Fecundity (# eggs)") + 
  scale_colour_manual(values = skisto_cols) + 
  theme(legend.position = "none")
```

```{r}
ggplot(ctmax_data, aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x)) + 
  geom_point()+
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt()

```

Data for just *Skistodiaptomus pallidus* is shown below. Point color is arranged according to latitude. 
```{r fig.width=8.5, fig.height=5.5}
ctmax_data %>% 
  filter(species == "skistodiaptomus_pallidus") %>%
  mutate(site = fct_reorder(site, lat, .desc = T)) %>% 
  # group_by(site) %>% 
  # summarise(size = mean(size, na.rm = T), 
  #          total_egg_volume = mean(total_egg_volume, na.rm = T)) %>% 
  ggplot(aes(x = size, y = total_egg_volume)) + 
  geom_smooth(method = "lm", formula = y ~ exp(x), 
              colour = "black") + 
  geom_point(aes(colour = site))+
  scale_color_viridis_d(direction = 1, 
                        option = "D") + 
  labs(x = "Prosome Length (mm)",
       y = "Total Egg Volume (mm^3)") + 
  theme_matt() + 
  theme(legend.position = "right")
```


```{r}
model_data = ctmax_data %>% 
  mutate("genus" = str_split_fixed(species, pattern = "_", n = 2)[,1],
         genus = tools::toTitleCase(genus),
         "doy" = yday(collection_date)) %>% 
  select(site, collection_date, doy, collection_temp, lat, elevation, species, genus, sample_id, fecundity, total_egg_volume, size, ctmax) %>% 
  filter(genus != "MH") %>%  
  mutate(total_egg_volume = if_else(is.na(total_egg_volume), 0, total_egg_volume),
         collection_temp_sc = scale(collection_temp),
         lat_sc = scale(lat), 
         elevation_sc = scale(elevation),
         tev_sc = scale(total_egg_volume)) 

ctmax_overall.model = lm(data = model_data, 
                      ctmax ~ genus + collection_temp + lat + elevation + total_egg_volume)

drop1(ctmax_overall.model, test = "F")

#MuMIn::dredge(ctmax_temp.model)

performance::check_model(ctmax_overall.model)

summary(ctmax_overall.model)

emmeans::emmeans(ctmax_overall.model, specs = "genus") %>% 
  data.frame() %>% 
  mutate(genus = fct_reorder(genus, .x = emmean, .desc = T)) %>% 
  ggplot(aes(genus, y = emmean)) + 
  geom_point(size = 4) + 
  geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), 
                width = 0.2, linewidth = 1) + 
  labs(x = "") + 
  theme_matt() + 
  theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))
```

```{r}

ctmax_temp.model = lm(data = model_data, 
                      ctmax ~ species * collection_temp)

drop1(ctmax_temp.model, 
      scope = ~.,
      test = "F")

performance::check_model(ctmax_temp.model)

sp_means = emmeans::emmeans(ctmax_temp.model, "species") %>% 
  data.frame() %>% 
  drop_na() %>%  
  select(species, "ctmax" = emmean)

sp_plast = emmeans::emtrends(ctmax_temp.model, "species", var = "collection_temp") %>% 
  data.frame() %>% 
  drop_na() %>% 
  mutate(species = fct_reorder(species, .x = collection_temp.trend, .desc = T)) %>% 
  select(species, "plast" = collection_temp.trend)

sp_means %>% 
inner_join(sp_plast) %>% 
  mutate(species = fct_reorder(species, plast, .fun = min)) %>% 
  ggplot(aes(x = species, y = plast)) + 
  geom_hline(yintercept = 0) + 
  geom_bar(stat = "identity") + 
  theme_matt() + 
  theme(axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))
```



```{r fecundity-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = fecundity, y = site, fill = site)) + 
  geom_density_ridges(bandwidth = 2,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r size-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = size, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  theme_matt() + 
  theme(legend.position = "none")
```

```{r ctmax-ridges, fig.width=8, fig.height=8}
ctmax_data %>% 
  mutate(group_id = paste(site, species)) %>% 
  ggplot(aes(x = ctmax, y = site, fill = site, group = group_id)) + 
  geom_density_ridges(bandwidth = 0.3,
                      jittered_points = TRUE, 
                      point_shape = 21,
                      point_size = 1,
                      point_colour = "grey30",
                      point_alpha = 0.6,
                      alpha = 0.9,
                      position = position_points_jitter(
                        height = 0.1, width = 0)) + 
  scale_fill_viridis_d(option = "D", direction = -1) + 
  labs(x = "CTmax (°C)") + 
  theme_matt() + 
  theme(legend.position = "none")
```

## High throughput size measurements 



```{r}
scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>% 
  ggplot(aes(x = length, fill = species)) + 
  facet_wrap(site~., nrow = 3) + 
  geom_histogram(binwidth = 0.01, colour = "grey10", linewidth = 0.25) + 
  scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")
```

```{r}

scan_sizes %>% 
  filter(sex == "female", stage == "adult") %>%
  ggplot(aes(x = length, y = site, fill = species)) + 
  geom_density_ridges(bandwidth = 0.02,
                      jittered_points = TRUE,
                      position = position_points_jitter(yoffset = -0.15, width = 0, height = 0.1),
                      point_alpha = 0.3, point_colour = "grey30")  + 
  labs(y = "",
       x = "Prosome Length (mm)") + 
    scale_fill_manual(values = skisto_cols) + 
  theme_minimal(base_size = 20) + 
  theme(panel.grid = element_blank(),
        strip.text = element_text(face = "bold"),
        legend.position = "bottom")
  
```


## COI Barcoding 

```{r, fig.align="center"}
knitr::include_graphics("../Figures/species_prop_plot.png")
```

```{r, fig.align="center"}
knitr::include_graphics("../Figures/clade_prop_plot.png")
```

```{r, fig.align="center"}
knitr::include_graphics("../Figures/tree_plot.png")
```
